{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e634a9d9",
   "metadata": {},
   "source": [
    "# ResNet\n",
    "\n",
    "在 **ResNet（Residual Network, 残差网络）** 提出之前，深度卷积神经网络（CNN）面临一个关键问题：**随着网络层数的增加，模型的性能不升反降**。这种现象被称为**退化问题（Degradation Problem）**，即更深的网络在训练集和测试集上的表现反而比浅层网络更差。\n",
    "\n",
    "传统观点认为，更深的网络可以学习更复杂的特征，但实验表明，深层网络的优化难度显著增加，梯度消失/爆炸（尽管通过BatchNorm缓解了部分问题）和网络退化成为主要瓶颈。2015年，何恺明团队提出的**ResNet**（CVPR 2016最佳论文）通过引入**残差连接（Residual Connection）**，成功训练了超过1000层的网络，并在ImageNet分类任务上取得突破性进展（Top-5错误率降至3.57%）。\n",
    "\n",
    "**ResNet解决的核心问题**：\n",
    "- **梯度传播难题**：通过残差结构，梯度可以直接绕过非线性层回传，缓解梯度消失。\n",
    "- **网络退化**：即使深层网络的额外层恒等映射（Identity Mapping），性能也不应比浅层网络差，残差结构确保了这一点。\n",
    "\n",
    "---\n",
    "\n",
    "### 创新性\n",
    "- **残差学习（Residual Learning）**  \n",
    "  传统网络直接学习目标函数 $ H(x)$，而ResNet改为学习残差 $ F(x) = H(x) - x $，并通过跳跃连接（Shortcut Connection）实现 $H(x) = F(x) + x$。这种设计使得网络更容易学习微小变化（如恒等映射时 $F(x) \\to 0$）。\n",
    "\n",
    "  ![alt text](resources/residual_connection.png \"Title\")\n",
    "  \n",
    "- **Bottleneck结构**  \n",
    "  在深层ResNet（如ResNet-50/101）中，使用1×1卷积先降维再升维，减少计算量（如3×3卷积的输入/输出通道数降低）。\n",
    "\n",
    "### 对当代CNN的影响\n",
    "- **成为深度网络的标配**  \n",
    "  ResNet的残差结构被广泛应用于几乎所有现代CNN架构（如DenseNet、MobileNetV2、EfficientNet等），成为构建深层模型的基础组件。\n",
    "  \n",
    "- **推动网络深度飞跃**  \n",
    "  ResNet-152（2015年）的提出证明了极深网络的可行性，后续研究（如ResNeXt、Res2Net）进一步优化了残差模块的设计。\n",
    "  \n",
    "- **跨领域应用**  \n",
    "  残差思想被迁移到其他架构（如Transformer中的残差连接、U-Net的跳跃连接），甚至影响了非神经网络模型（如残差强化学习）。\n",
    "\n",
    "\n",
    "ResNet通过简单的残差连接解决了深层网络的训练难题，成为深度学习史上的里程碑。其设计哲学（“让网络更容易学习恒等映射”）启发了后续大量研究，至今仍是计算机视觉和其他领域的核心架构之一。\n",
    "\n",
    "  ![alt text](resources/resnet_arch.png \"Title\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c1e233",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa930696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37a7794b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.data_util.transforms import RandomResize\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        RandomResize([256, 296, 384]),  # 随机在三个size中选择一个进行resize\n",
    "        transforms.RandomRotation(10),\n",
    "        transforms.RandomCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "val_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=train_dataset_transforms,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=val_dataset_transforms,\n",
    ")\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ba1130",
   "metadata": {},
   "source": [
    "## 测试比较不同的Resnet架构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "63fd73c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/150 Train Loss: 1.9133 Accuracy: 0.3382 Time: 7.24565  | Val Loss: 1.8480 Accuracy: 0.3873\n",
      "Epoch: 2/150 Train Loss: 1.6093 Accuracy: 0.4613 Time: 7.09541  | Val Loss: 2.0834 Accuracy: 0.3873\n",
      "Epoch: 3/150 Train Loss: 1.3935 Accuracy: 0.5401 Time: 7.05743  | Val Loss: 2.0390 Accuracy: 0.4117\n",
      "Epoch: 4/150 Train Loss: 1.2248 Accuracy: 0.6039 Time: 7.08256  | Val Loss: 1.3004 Accuracy: 0.5934\n",
      "Epoch: 5/150 Train Loss: 1.1614 Accuracy: 0.6230 Time: 7.02096  | Val Loss: 1.2646 Accuracy: 0.5952\n",
      "Epoch: 6/150 Train Loss: 1.0868 Accuracy: 0.6572 Time: 7.51722  | Val Loss: 1.0496 Accuracy: 0.6685\n",
      "Epoch: 7/150 Train Loss: 0.9813 Accuracy: 0.6829 Time: 7.08420  | Val Loss: 0.9686 Accuracy: 0.6930\n",
      "Epoch: 8/150 Train Loss: 0.9388 Accuracy: 0.7013 Time: 6.98512  | Val Loss: 0.9658 Accuracy: 0.6859\n",
      "Epoch: 9/150 Train Loss: 0.9012 Accuracy: 0.7107 Time: 7.03298  | Val Loss: 0.7795 Accuracy: 0.7536\n",
      "Epoch: 10/150 Train Loss: 0.8494 Accuracy: 0.7307 Time: 7.34997  | Val Loss: 1.0847 Accuracy: 0.6757\n",
      "Epoch: 11/150 Train Loss: 0.8114 Accuracy: 0.7422 Time: 7.15098  | Val Loss: 0.8723 Accuracy: 0.7141\n",
      "Epoch: 12/150 Train Loss: 0.7728 Accuracy: 0.7525 Time: 7.06411  | Val Loss: 0.8092 Accuracy: 0.7401\n",
      "Epoch: 13/150 Train Loss: 0.7330 Accuracy: 0.7672 Time: 7.03134  | Val Loss: 0.7492 Accuracy: 0.7605\n",
      "Epoch: 14/150 Train Loss: 0.7038 Accuracy: 0.7789 Time: 6.97486  | Val Loss: 0.7570 Accuracy: 0.7710\n",
      "Epoch: 15/150 Train Loss: 0.7150 Accuracy: 0.7739 Time: 6.99472  | Val Loss: 0.7263 Accuracy: 0.7745\n",
      "Epoch: 16/150 Train Loss: 0.6600 Accuracy: 0.7917 Time: 6.98829  | Val Loss: 0.8699 Accuracy: 0.7406\n",
      "Epoch: 17/150 Train Loss: 0.6630 Accuracy: 0.7923 Time: 7.03304  | Val Loss: 1.0465 Accuracy: 0.6871\n",
      "Epoch: 18/150 Train Loss: 0.6353 Accuracy: 0.7978 Time: 7.05066  | Val Loss: 0.5995 Accuracy: 0.8092\n",
      "Epoch: 19/150 Train Loss: 0.6022 Accuracy: 0.8093 Time: 6.99797  | Val Loss: 0.7416 Accuracy: 0.7738\n",
      "Epoch: 20/150 Train Loss: 0.5864 Accuracy: 0.8091 Time: 7.07123  | Val Loss: 0.7238 Accuracy: 0.7873\n",
      "Epoch: 21/150 Train Loss: 0.5815 Accuracy: 0.8152 Time: 7.03867  | Val Loss: 0.6339 Accuracy: 0.8138\n",
      "Epoch: 22/150 Train Loss: 0.5389 Accuracy: 0.8297 Time: 7.06256  | Val Loss: 0.6253 Accuracy: 0.8066\n",
      "Epoch: 23/150 Train Loss: 0.5319 Accuracy: 0.8281 Time: 6.97909  | Val Loss: 0.5906 Accuracy: 0.8140\n",
      "Epoch: 24/150 Train Loss: 0.5357 Accuracy: 0.8308 Time: 7.03158  | Val Loss: 0.5588 Accuracy: 0.8306\n",
      "Epoch: 25/150 Train Loss: 0.5132 Accuracy: 0.8349 Time: 7.12925  | Val Loss: 0.5536 Accuracy: 0.8298\n",
      "Epoch: 26/150 Train Loss: 0.5185 Accuracy: 0.8362 Time: 7.03500  | Val Loss: 0.5946 Accuracy: 0.8250\n",
      "Epoch: 27/150 Train Loss: 0.4915 Accuracy: 0.8445 Time: 7.07049  | Val Loss: 0.6172 Accuracy: 0.8112\n",
      "Epoch: 28/150 Train Loss: 0.4819 Accuracy: 0.8445 Time: 7.02433  | Val Loss: 0.5275 Accuracy: 0.8441\n",
      "Epoch: 29/150 Train Loss: 0.4570 Accuracy: 0.8549 Time: 7.04026  | Val Loss: 0.4730 Accuracy: 0.8558\n",
      "Epoch: 30/150 Train Loss: 0.4607 Accuracy: 0.8504 Time: 7.09286  | Val Loss: 0.6675 Accuracy: 0.8092\n",
      "Epoch: 31/150 Train Loss: 0.4013 Accuracy: 0.8745 Time: 7.03665  | Val Loss: 0.4738 Accuracy: 0.8606\n",
      "Epoch: 32/150 Train Loss: 0.3647 Accuracy: 0.8816 Time: 7.10495  | Val Loss: 0.4991 Accuracy: 0.8476\n",
      "Epoch: 33/150 Train Loss: 0.3443 Accuracy: 0.8880 Time: 7.08982  | Val Loss: 0.4560 Accuracy: 0.8701\n",
      "Epoch: 34/150 Train Loss: 0.3212 Accuracy: 0.8981 Time: 7.08455  | Val Loss: 0.4288 Accuracy: 0.8736\n",
      "Epoch: 35/150 Train Loss: 0.3298 Accuracy: 0.8947 Time: 7.08427  | Val Loss: 0.4358 Accuracy: 0.8754\n",
      "Epoch: 36/150 Train Loss: 0.3281 Accuracy: 0.8960 Time: 7.04801  | Val Loss: 0.4559 Accuracy: 0.8639\n",
      "Epoch: 37/150 Train Loss: 0.3062 Accuracy: 0.9010 Time: 7.02579  | Val Loss: 0.4917 Accuracy: 0.8680\n",
      "Epoch: 38/150 Train Loss: 0.2914 Accuracy: 0.9078 Time: 6.97646  | Val Loss: 0.4473 Accuracy: 0.8752\n",
      "Epoch: 39/150 Train Loss: 0.3028 Accuracy: 0.9007 Time: 7.05335  | Val Loss: 0.4755 Accuracy: 0.8711\n",
      "Epoch: 40/150 Train Loss: 0.2976 Accuracy: 0.9038 Time: 7.01375  | Val Loss: 0.3963 Accuracy: 0.8828\n",
      "Epoch: 41/150 Train Loss: 0.2968 Accuracy: 0.9064 Time: 7.01746  | Val Loss: 0.5057 Accuracy: 0.8555\n",
      "Epoch: 42/150 Train Loss: 0.2896 Accuracy: 0.9069 Time: 7.03936  | Val Loss: 0.4805 Accuracy: 0.8627\n",
      "Epoch: 43/150 Train Loss: 0.2906 Accuracy: 0.9042 Time: 7.03989  | Val Loss: 0.4250 Accuracy: 0.8744\n",
      "Epoch: 44/150 Train Loss: 0.2709 Accuracy: 0.9127 Time: 6.87121  | Val Loss: 0.4222 Accuracy: 0.8792\n",
      "Epoch: 45/150 Train Loss: 0.2742 Accuracy: 0.9148 Time: 6.84154  | Val Loss: 0.5012 Accuracy: 0.8642\n",
      "Epoch: 46/150 Train Loss: 0.2649 Accuracy: 0.9126 Time: 6.83428  | Val Loss: 0.4024 Accuracy: 0.8800\n",
      "Epoch: 47/150 Train Loss: 0.2626 Accuracy: 0.9141 Time: 6.87729  | Val Loss: 0.4678 Accuracy: 0.8749\n",
      "Epoch: 48/150 Train Loss: 0.2731 Accuracy: 0.9100 Time: 6.89092  | Val Loss: 0.4289 Accuracy: 0.8775\n",
      "Epoch: 49/150 Train Loss: 0.2517 Accuracy: 0.9202 Time: 6.87282  | Val Loss: 0.4894 Accuracy: 0.8696\n",
      "Epoch: 50/150 Train Loss: 0.2457 Accuracy: 0.9212 Time: 6.84240  | Val Loss: 0.4639 Accuracy: 0.8693\n",
      "Epoch: 51/150 Train Loss: 0.2470 Accuracy: 0.9190 Time: 6.87475  | Val Loss: 0.4868 Accuracy: 0.8724\n",
      "Epoch: 52/150 Train Loss: 0.2416 Accuracy: 0.9245 Time: 6.86457  | Val Loss: 0.4467 Accuracy: 0.8797\n",
      "Epoch: 53/150 Train Loss: 0.2386 Accuracy: 0.9230 Time: 6.84563  | Val Loss: 0.4699 Accuracy: 0.8708\n",
      "Epoch: 54/150 Train Loss: 0.2338 Accuracy: 0.9242 Time: 6.83803  | Val Loss: 0.4118 Accuracy: 0.8871\n",
      "Epoch: 55/150 Train Loss: 0.2264 Accuracy: 0.9277 Time: 6.84622  | Val Loss: 0.4632 Accuracy: 0.8782\n",
      "Epoch: 56/150 Train Loss: 0.2290 Accuracy: 0.9276 Time: 6.85841  | Val Loss: 0.4729 Accuracy: 0.8754\n",
      "Epoch: 57/150 Train Loss: 0.2200 Accuracy: 0.9303 Time: 6.86955  | Val Loss: 0.4635 Accuracy: 0.8739\n",
      "Epoch: 58/150 Train Loss: 0.2152 Accuracy: 0.9304 Time: 6.87616  | Val Loss: 0.5361 Accuracy: 0.8627\n",
      "Epoch: 59/150 Train Loss: 0.2220 Accuracy: 0.9276 Time: 6.85301  | Val Loss: 0.5114 Accuracy: 0.8629\n",
      "Epoch: 60/150 Train Loss: 0.2128 Accuracy: 0.9300 Time: 6.90574  | Val Loss: 0.5196 Accuracy: 0.8611\n",
      "Epoch: 61/150 Train Loss: 0.1759 Accuracy: 0.9416 Time: 6.82562  | Val Loss: 0.3997 Accuracy: 0.8917\n",
      "Epoch: 62/150 Train Loss: 0.1618 Accuracy: 0.9491 Time: 6.83937  | Val Loss: 0.3972 Accuracy: 0.8907\n",
      "Epoch: 63/150 Train Loss: 0.1539 Accuracy: 0.9504 Time: 6.84418  | Val Loss: 0.4148 Accuracy: 0.8932\n",
      "Epoch: 64/150 Train Loss: 0.1508 Accuracy: 0.9516 Time: 6.83973  | Val Loss: 0.4158 Accuracy: 0.8907\n",
      "Epoch: 65/150 Train Loss: 0.1384 Accuracy: 0.9566 Time: 6.88155  | Val Loss: 0.4319 Accuracy: 0.8894\n",
      "Epoch: 66/150 Train Loss: 0.1409 Accuracy: 0.9546 Time: 6.83626  | Val Loss: 0.4403 Accuracy: 0.8859\n",
      "Epoch: 67/150 Train Loss: 0.1385 Accuracy: 0.9563 Time: 6.81021  | Val Loss: 0.4409 Accuracy: 0.8899\n",
      "Epoch: 68/150 Train Loss: 0.1417 Accuracy: 0.9561 Time: 6.86279  | Val Loss: 0.4210 Accuracy: 0.8927\n",
      "Epoch: 69/150 Train Loss: 0.1398 Accuracy: 0.9555 Time: 6.82430  | Val Loss: 0.4408 Accuracy: 0.8866\n",
      "Epoch: 70/150 Train Loss: 0.1395 Accuracy: 0.9551 Time: 6.88855  | Val Loss: 0.4334 Accuracy: 0.8874\n",
      "Early stop at epoch 70!\n",
      "#Parameter: 11181642 Accuracy: 0.8828025477707007\n"
     ]
    }
   ],
   "source": [
    "from spacy import training\n",
    "from hdd.models.cnn.resnet import Resnet, resnet18_config\n",
    "from hdd.train.early_stopping import EarlyStoppingInMem\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    resnet_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout,\n",
    "    lr,\n",
    "    weight_decay,\n",
    "    step_size=30,\n",
    "    gamma=0.5,\n",
    "    patience=30,\n",
    "    max_epochs=150,\n",
    ") -> tuple[Resnet, dict[str, list[float]]]:\n",
    "    net = Resnet(resnet_config, num_classes=10, dropout=dropout).to(DEVICE)\n",
    "    criteria = nn.CrossEntropyLoss()\n",
    "    # SGD的效果远不如Adam好\n",
    "    # optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9)\n",
    "    optimizer = optim.AdamW(\n",
    "        net.parameters(), lr=lr, eps=1e-6, weight_decay=weight_decay\n",
    "    )\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "        optimizer, step_size=step_size, gamma=gamma, last_epoch=-1\n",
    "    )\n",
    "    early_stopper = EarlyStoppingInMem(patience=patience, verbose=False)\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        early_stopper,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return net, training_stats\n",
    "\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(128, train_dataset, val_dataset)\n",
    "net, resnet18_stats = train_net(\n",
    "    resnet18_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-4,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "13ecf683",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/150 Train Loss: 1.8986 Accuracy: 0.3413 Time: 9.69959  | Val Loss: 1.8952 Accuracy: 0.3969\n",
      "Epoch: 2/150 Train Loss: 1.5854 Accuracy: 0.4682 Time: 9.71893  | Val Loss: 1.5856 Accuracy: 0.4703\n",
      "Epoch: 3/150 Train Loss: 1.4127 Accuracy: 0.5391 Time: 9.69183  | Val Loss: 1.3682 Accuracy: 0.5559\n",
      "Epoch: 4/150 Train Loss: 1.2494 Accuracy: 0.5989 Time: 9.70857  | Val Loss: 1.1710 Accuracy: 0.6262\n",
      "Epoch: 5/150 Train Loss: 1.1400 Accuracy: 0.6293 Time: 9.68955  | Val Loss: 1.0006 Accuracy: 0.6752\n",
      "Epoch: 6/150 Train Loss: 1.0561 Accuracy: 0.6626 Time: 9.69012  | Val Loss: 1.1600 Accuracy: 0.6380\n",
      "Epoch: 7/150 Train Loss: 0.9957 Accuracy: 0.6835 Time: 9.69107  | Val Loss: 0.9895 Accuracy: 0.6797\n",
      "Epoch: 8/150 Train Loss: 0.9502 Accuracy: 0.6982 Time: 9.68394  | Val Loss: 1.3597 Accuracy: 0.5715\n",
      "Epoch: 9/150 Train Loss: 0.9099 Accuracy: 0.7111 Time: 9.68502  | Val Loss: 0.9546 Accuracy: 0.6907\n",
      "Epoch: 10/150 Train Loss: 0.8396 Accuracy: 0.7315 Time: 9.68136  | Val Loss: 0.9914 Accuracy: 0.7062\n",
      "Epoch: 11/150 Train Loss: 0.8403 Accuracy: 0.7358 Time: 9.68155  | Val Loss: 0.9225 Accuracy: 0.7259\n",
      "Epoch: 12/150 Train Loss: 0.7710 Accuracy: 0.7576 Time: 9.73533  | Val Loss: 1.1368 Accuracy: 0.6680\n",
      "Epoch: 13/150 Train Loss: 0.7302 Accuracy: 0.7724 Time: 9.65536  | Val Loss: 0.7514 Accuracy: 0.7692\n",
      "Epoch: 14/150 Train Loss: 0.7221 Accuracy: 0.7692 Time: 9.69563  | Val Loss: 0.6777 Accuracy: 0.7880\n",
      "Epoch: 15/150 Train Loss: 0.7070 Accuracy: 0.7757 Time: 9.68890  | Val Loss: 0.9548 Accuracy: 0.7039\n",
      "Epoch: 16/150 Train Loss: 0.6731 Accuracy: 0.7831 Time: 9.67604  | Val Loss: 0.6266 Accuracy: 0.8025\n",
      "Epoch: 17/150 Train Loss: 0.6473 Accuracy: 0.7922 Time: 9.65970  | Val Loss: 0.6852 Accuracy: 0.7939\n",
      "Epoch: 18/150 Train Loss: 0.6435 Accuracy: 0.7965 Time: 9.73018  | Val Loss: 0.6830 Accuracy: 0.7883\n",
      "Epoch: 19/150 Train Loss: 0.6138 Accuracy: 0.8106 Time: 9.72872  | Val Loss: 0.5676 Accuracy: 0.8194\n",
      "Epoch: 20/150 Train Loss: 0.6069 Accuracy: 0.8106 Time: 9.68737  | Val Loss: 0.6181 Accuracy: 0.8089\n",
      "Epoch: 21/150 Train Loss: 0.5739 Accuracy: 0.8177 Time: 9.76213  | Val Loss: 0.7172 Accuracy: 0.7809\n",
      "Epoch: 22/150 Train Loss: 0.5737 Accuracy: 0.8171 Time: 9.67775  | Val Loss: 0.5536 Accuracy: 0.8234\n",
      "Epoch: 23/150 Train Loss: 0.5542 Accuracy: 0.8253 Time: 9.68689  | Val Loss: 0.6393 Accuracy: 0.8130\n",
      "Epoch: 24/150 Train Loss: 0.5224 Accuracy: 0.8316 Time: 9.66860  | Val Loss: 0.5436 Accuracy: 0.8250\n",
      "Epoch: 25/150 Train Loss: 0.5162 Accuracy: 0.8349 Time: 9.71260  | Val Loss: 0.5008 Accuracy: 0.8448\n",
      "Epoch: 26/150 Train Loss: 0.4973 Accuracy: 0.8413 Time: 9.68413  | Val Loss: 0.6765 Accuracy: 0.8099\n",
      "Epoch: 27/150 Train Loss: 0.4935 Accuracy: 0.8428 Time: 9.67082  | Val Loss: 0.5708 Accuracy: 0.8247\n",
      "Epoch: 28/150 Train Loss: 0.4792 Accuracy: 0.8447 Time: 9.67997  | Val Loss: 0.4905 Accuracy: 0.8482\n",
      "Epoch: 29/150 Train Loss: 0.4807 Accuracy: 0.8479 Time: 9.66073  | Val Loss: 0.6529 Accuracy: 0.8054\n",
      "Epoch: 30/150 Train Loss: 0.4705 Accuracy: 0.8521 Time: 9.67862  | Val Loss: 0.5044 Accuracy: 0.8451\n",
      "Epoch: 31/150 Train Loss: 0.3865 Accuracy: 0.8783 Time: 9.70095  | Val Loss: 0.3932 Accuracy: 0.8823\n",
      "Epoch: 32/150 Train Loss: 0.3523 Accuracy: 0.8874 Time: 9.66897  | Val Loss: 0.5875 Accuracy: 0.8237\n",
      "Epoch: 33/150 Train Loss: 0.3360 Accuracy: 0.8950 Time: 9.69074  | Val Loss: 0.4184 Accuracy: 0.8764\n",
      "Epoch: 34/150 Train Loss: 0.3290 Accuracy: 0.8926 Time: 9.66508  | Val Loss: 0.4219 Accuracy: 0.8657\n",
      "Epoch: 35/150 Train Loss: 0.3244 Accuracy: 0.8956 Time: 9.73085  | Val Loss: 0.4734 Accuracy: 0.8601\n",
      "Epoch: 36/150 Train Loss: 0.3237 Accuracy: 0.8972 Time: 9.68934  | Val Loss: 0.4032 Accuracy: 0.8752\n",
      "Epoch: 37/150 Train Loss: 0.3100 Accuracy: 0.8995 Time: 9.65040  | Val Loss: 0.4805 Accuracy: 0.8538\n",
      "Epoch: 38/150 Train Loss: 0.3028 Accuracy: 0.9023 Time: 9.70645  | Val Loss: 0.4386 Accuracy: 0.8604\n",
      "Epoch: 39/150 Train Loss: 0.2926 Accuracy: 0.9019 Time: 9.70871  | Val Loss: 0.4659 Accuracy: 0.8668\n",
      "Epoch: 40/150 Train Loss: 0.3109 Accuracy: 0.8985 Time: 9.66696  | Val Loss: 0.5843 Accuracy: 0.8352\n",
      "Epoch: 41/150 Train Loss: 0.2931 Accuracy: 0.9081 Time: 9.66825  | Val Loss: 0.4396 Accuracy: 0.8645\n",
      "Epoch: 42/150 Train Loss: 0.2725 Accuracy: 0.9104 Time: 9.70741  | Val Loss: 0.4325 Accuracy: 0.8741\n",
      "Epoch: 43/150 Train Loss: 0.2834 Accuracy: 0.9057 Time: 9.67498  | Val Loss: 0.3860 Accuracy: 0.8828\n",
      "Epoch: 44/150 Train Loss: 0.2701 Accuracy: 0.9184 Time: 9.69479  | Val Loss: 0.5135 Accuracy: 0.8662\n",
      "Epoch: 45/150 Train Loss: 0.2741 Accuracy: 0.9109 Time: 9.70679  | Val Loss: 0.3916 Accuracy: 0.8864\n",
      "Epoch: 46/150 Train Loss: 0.2527 Accuracy: 0.9196 Time: 9.68718  | Val Loss: 0.4278 Accuracy: 0.8759\n",
      "Epoch: 47/150 Train Loss: 0.2640 Accuracy: 0.9170 Time: 9.67701  | Val Loss: 0.4683 Accuracy: 0.8685\n",
      "Epoch: 48/150 Train Loss: 0.2588 Accuracy: 0.9165 Time: 9.68031  | Val Loss: 0.4295 Accuracy: 0.8790\n",
      "Epoch: 49/150 Train Loss: 0.2554 Accuracy: 0.9188 Time: 9.71105  | Val Loss: 0.5006 Accuracy: 0.8611\n",
      "Epoch: 50/150 Train Loss: 0.2535 Accuracy: 0.9185 Time: 9.65609  | Val Loss: 0.4423 Accuracy: 0.8780\n",
      "Epoch: 51/150 Train Loss: 0.2557 Accuracy: 0.9168 Time: 9.65590  | Val Loss: 0.4655 Accuracy: 0.8662\n",
      "Epoch: 52/150 Train Loss: 0.2491 Accuracy: 0.9188 Time: 9.74026  | Val Loss: 0.4019 Accuracy: 0.8815\n",
      "Epoch: 53/150 Train Loss: 0.2497 Accuracy: 0.9203 Time: 9.66833  | Val Loss: 0.4679 Accuracy: 0.8688\n",
      "Epoch: 54/150 Train Loss: 0.2417 Accuracy: 0.9255 Time: 9.68286  | Val Loss: 0.4866 Accuracy: 0.8642\n",
      "Epoch: 55/150 Train Loss: 0.2319 Accuracy: 0.9249 Time: 9.67352  | Val Loss: 0.4907 Accuracy: 0.8718\n",
      "Epoch: 56/150 Train Loss: 0.2244 Accuracy: 0.9309 Time: 9.71286  | Val Loss: 0.5048 Accuracy: 0.8683\n",
      "Epoch: 57/150 Train Loss: 0.2134 Accuracy: 0.9289 Time: 9.68367  | Val Loss: 0.4261 Accuracy: 0.8818\n",
      "Epoch: 58/150 Train Loss: 0.2144 Accuracy: 0.9289 Time: 9.72506  | Val Loss: 0.4674 Accuracy: 0.8741\n",
      "Epoch: 59/150 Train Loss: 0.2056 Accuracy: 0.9323 Time: 9.69571  | Val Loss: 0.4980 Accuracy: 0.8637\n",
      "Epoch: 60/150 Train Loss: 0.2172 Accuracy: 0.9318 Time: 9.69395  | Val Loss: 0.4099 Accuracy: 0.8884\n",
      "Epoch: 61/150 Train Loss: 0.1674 Accuracy: 0.9454 Time: 9.70444  | Val Loss: 0.3711 Accuracy: 0.8927\n",
      "Epoch: 62/150 Train Loss: 0.1572 Accuracy: 0.9492 Time: 9.66413  | Val Loss: 0.4039 Accuracy: 0.8920\n",
      "Epoch: 63/150 Train Loss: 0.1510 Accuracy: 0.9524 Time: 9.67127  | Val Loss: 0.3980 Accuracy: 0.8922\n",
      "Epoch: 64/150 Train Loss: 0.1524 Accuracy: 0.9507 Time: 9.67778  | Val Loss: 0.4274 Accuracy: 0.8907\n",
      "Epoch: 65/150 Train Loss: 0.1452 Accuracy: 0.9555 Time: 9.74850  | Val Loss: 0.4022 Accuracy: 0.8912\n",
      "Epoch: 66/150 Train Loss: 0.1452 Accuracy: 0.9529 Time: 9.67768  | Val Loss: 0.4298 Accuracy: 0.8879\n",
      "Epoch: 67/150 Train Loss: 0.1478 Accuracy: 0.9519 Time: 9.69273  | Val Loss: 0.4076 Accuracy: 0.8910\n",
      "Epoch: 68/150 Train Loss: 0.1353 Accuracy: 0.9567 Time: 9.72048  | Val Loss: 0.3948 Accuracy: 0.8953\n",
      "Epoch: 69/150 Train Loss: 0.1417 Accuracy: 0.9551 Time: 9.74951  | Val Loss: 0.4438 Accuracy: 0.8887\n",
      "Epoch: 70/150 Train Loss: 0.1418 Accuracy: 0.9554 Time: 9.67550  | Val Loss: 0.4020 Accuracy: 0.8912\n",
      "Epoch: 71/150 Train Loss: 0.1366 Accuracy: 0.9567 Time: 9.68670  | Val Loss: 0.4265 Accuracy: 0.8879\n",
      "Epoch: 72/150 Train Loss: 0.1322 Accuracy: 0.9555 Time: 9.68767  | Val Loss: 0.4243 Accuracy: 0.8958\n",
      "Epoch: 73/150 Train Loss: 0.1264 Accuracy: 0.9603 Time: 9.69102  | Val Loss: 0.4121 Accuracy: 0.8925\n",
      "Epoch: 74/150 Train Loss: 0.1243 Accuracy: 0.9608 Time: 9.69274  | Val Loss: 0.4240 Accuracy: 0.8948\n",
      "Epoch: 75/150 Train Loss: 0.1269 Accuracy: 0.9615 Time: 9.68753  | Val Loss: 0.4001 Accuracy: 0.8963\n",
      "Epoch: 76/150 Train Loss: 0.1261 Accuracy: 0.9613 Time: 9.73317  | Val Loss: 0.4428 Accuracy: 0.8864\n",
      "Epoch: 77/150 Train Loss: 0.1360 Accuracy: 0.9555 Time: 9.67919  | Val Loss: 0.4567 Accuracy: 0.8833\n",
      "Epoch: 78/150 Train Loss: 0.1272 Accuracy: 0.9590 Time: 9.70627  | Val Loss: 0.4264 Accuracy: 0.8915\n",
      "Epoch: 79/150 Train Loss: 0.1247 Accuracy: 0.9606 Time: 9.65606  | Val Loss: 0.4317 Accuracy: 0.8884\n",
      "Epoch: 80/150 Train Loss: 0.1180 Accuracy: 0.9630 Time: 9.71616  | Val Loss: 0.4522 Accuracy: 0.8887\n",
      "Epoch: 81/150 Train Loss: 0.1070 Accuracy: 0.9654 Time: 9.67727  | Val Loss: 0.4573 Accuracy: 0.8892\n",
      "Epoch: 82/150 Train Loss: 0.1183 Accuracy: 0.9632 Time: 9.64591  | Val Loss: 0.4519 Accuracy: 0.8848\n",
      "Epoch: 83/150 Train Loss: 0.1087 Accuracy: 0.9654 Time: 9.68306  | Val Loss: 0.4395 Accuracy: 0.8874\n",
      "Epoch: 84/150 Train Loss: 0.1115 Accuracy: 0.9665 Time: 9.68675  | Val Loss: 0.4250 Accuracy: 0.8961\n",
      "Epoch: 85/150 Train Loss: 0.1224 Accuracy: 0.9631 Time: 9.74996  | Val Loss: 0.5027 Accuracy: 0.8854\n",
      "Epoch: 86/150 Train Loss: 0.1218 Accuracy: 0.9588 Time: 9.66116  | Val Loss: 0.4706 Accuracy: 0.8833\n",
      "Epoch: 87/150 Train Loss: 0.1195 Accuracy: 0.9645 Time: 9.67127  | Val Loss: 0.4418 Accuracy: 0.8912\n",
      "Epoch: 88/150 Train Loss: 0.1006 Accuracy: 0.9660 Time: 9.67806  | Val Loss: 0.4299 Accuracy: 0.8948\n",
      "Epoch: 89/150 Train Loss: 0.0948 Accuracy: 0.9721 Time: 9.71896  | Val Loss: 0.4485 Accuracy: 0.8935\n",
      "Epoch: 90/150 Train Loss: 0.1180 Accuracy: 0.9612 Time: 9.66984  | Val Loss: 0.4688 Accuracy: 0.8854\n",
      "Epoch: 91/150 Train Loss: 0.0945 Accuracy: 0.9700 Time: 9.69154  | Val Loss: 0.4413 Accuracy: 0.8973\n",
      "Early stop at epoch 91!\n",
      "#Parameter: 21289802 Accuracy: 0.8927388535031847\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet34_config\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(128, train_dataset, val_dataset)\n",
    "net, resnet34_stats = train_net(\n",
    "    resnet34_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-4,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47e497a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/150 Train Loss: 2.3594 Accuracy: 0.2090 Time: 15.81821  | Val Loss: 2.0423 Accuracy: 0.2601\n",
      "Epoch: 2/150 Train Loss: 1.9640 Accuracy: 0.3032 Time: 15.79861  | Val Loss: 1.9277 Accuracy: 0.3233\n",
      "Epoch: 3/150 Train Loss: 1.7646 Accuracy: 0.3866 Time: 15.80088  | Val Loss: 1.9916 Accuracy: 0.3246\n",
      "Epoch: 4/150 Train Loss: 1.6305 Accuracy: 0.4353 Time: 15.81049  | Val Loss: 1.5092 Accuracy: 0.4920\n",
      "Epoch: 5/150 Train Loss: 1.5343 Accuracy: 0.4850 Time: 15.82453  | Val Loss: 1.3905 Accuracy: 0.5348\n",
      "Epoch: 6/150 Train Loss: 1.3993 Accuracy: 0.5357 Time: 15.76372  | Val Loss: 1.4614 Accuracy: 0.5218\n",
      "Epoch: 7/150 Train Loss: 1.3413 Accuracy: 0.5578 Time: 15.79463  | Val Loss: 1.1937 Accuracy: 0.6239\n",
      "Epoch: 8/150 Train Loss: 1.2444 Accuracy: 0.5929 Time: 15.80908  | Val Loss: 1.2835 Accuracy: 0.5763\n",
      "Epoch: 9/150 Train Loss: 1.1787 Accuracy: 0.6167 Time: 15.77034  | Val Loss: 1.3170 Accuracy: 0.5890\n",
      "Epoch: 10/150 Train Loss: 1.0864 Accuracy: 0.6461 Time: 15.77599  | Val Loss: 1.0829 Accuracy: 0.6548\n",
      "Epoch: 11/150 Train Loss: 1.0476 Accuracy: 0.6577 Time: 15.78780  | Val Loss: 1.0393 Accuracy: 0.6897\n",
      "Epoch: 12/150 Train Loss: 0.9986 Accuracy: 0.6784 Time: 15.75806  | Val Loss: 1.0914 Accuracy: 0.6484\n",
      "Epoch: 13/150 Train Loss: 0.9426 Accuracy: 0.6982 Time: 15.80613  | Val Loss: 1.0109 Accuracy: 0.6754\n",
      "Epoch: 14/150 Train Loss: 0.9151 Accuracy: 0.7040 Time: 15.80965  | Val Loss: 0.8755 Accuracy: 0.7144\n",
      "Epoch: 15/150 Train Loss: 0.8910 Accuracy: 0.7181 Time: 15.77640  | Val Loss: 0.9246 Accuracy: 0.7106\n",
      "Epoch: 16/150 Train Loss: 0.8526 Accuracy: 0.7259 Time: 15.81079  | Val Loss: 0.8711 Accuracy: 0.7315\n",
      "Epoch: 17/150 Train Loss: 0.7976 Accuracy: 0.7436 Time: 15.81550  | Val Loss: 0.8714 Accuracy: 0.7325\n",
      "Epoch: 18/150 Train Loss: 0.7815 Accuracy: 0.7504 Time: 15.79509  | Val Loss: 0.8703 Accuracy: 0.7164\n",
      "Epoch: 19/150 Train Loss: 0.7731 Accuracy: 0.7570 Time: 15.81419  | Val Loss: 0.8968 Accuracy: 0.7103\n",
      "Epoch: 20/150 Train Loss: 0.7380 Accuracy: 0.7665 Time: 15.76079  | Val Loss: 0.7193 Accuracy: 0.7832\n",
      "Epoch: 21/150 Train Loss: 0.7320 Accuracy: 0.7662 Time: 15.79046  | Val Loss: 0.6757 Accuracy: 0.7918\n",
      "Epoch: 22/150 Train Loss: 0.7153 Accuracy: 0.7665 Time: 15.80889  | Val Loss: 1.1504 Accuracy: 0.6548\n",
      "Epoch: 23/150 Train Loss: 0.7223 Accuracy: 0.7676 Time: 15.79530  | Val Loss: 0.6475 Accuracy: 0.8046\n",
      "Epoch: 24/150 Train Loss: 0.6858 Accuracy: 0.7848 Time: 15.77526  | Val Loss: 0.7836 Accuracy: 0.7761\n",
      "Epoch: 25/150 Train Loss: 0.6521 Accuracy: 0.7949 Time: 15.75614  | Val Loss: 0.8154 Accuracy: 0.7310\n",
      "Epoch: 26/150 Train Loss: 0.6471 Accuracy: 0.7928 Time: 15.77021  | Val Loss: 0.5785 Accuracy: 0.8245\n",
      "Epoch: 27/150 Train Loss: 0.6285 Accuracy: 0.7986 Time: 15.82700  | Val Loss: 0.7479 Accuracy: 0.7850\n",
      "Epoch: 28/150 Train Loss: 0.6067 Accuracy: 0.8079 Time: 15.80813  | Val Loss: 0.7897 Accuracy: 0.7536\n",
      "Epoch: 29/150 Train Loss: 0.6262 Accuracy: 0.8040 Time: 15.77073  | Val Loss: 0.6047 Accuracy: 0.8122\n",
      "Epoch: 30/150 Train Loss: 0.6056 Accuracy: 0.8033 Time: 15.77409  | Val Loss: 0.7273 Accuracy: 0.7819\n",
      "Epoch: 31/150 Train Loss: 0.5116 Accuracy: 0.8366 Time: 15.78427  | Val Loss: 0.4954 Accuracy: 0.8418\n",
      "Epoch: 32/150 Train Loss: 0.4935 Accuracy: 0.8410 Time: 15.81132  | Val Loss: 0.4809 Accuracy: 0.8489\n",
      "Epoch: 33/150 Train Loss: 0.4702 Accuracy: 0.8463 Time: 15.81008  | Val Loss: 0.4510 Accuracy: 0.8622\n",
      "Epoch: 34/150 Train Loss: 0.4706 Accuracy: 0.8463 Time: 15.78206  | Val Loss: 0.4838 Accuracy: 0.8525\n",
      "Epoch: 35/150 Train Loss: 0.4632 Accuracy: 0.8492 Time: 15.79469  | Val Loss: 0.4515 Accuracy: 0.8561\n",
      "Epoch: 36/150 Train Loss: 0.4384 Accuracy: 0.8608 Time: 15.79928  | Val Loss: 0.4808 Accuracy: 0.8471\n",
      "Epoch: 37/150 Train Loss: 0.4343 Accuracy: 0.8591 Time: 15.79076  | Val Loss: 0.4977 Accuracy: 0.8420\n",
      "Epoch: 38/150 Train Loss: 0.4300 Accuracy: 0.8606 Time: 15.78999  | Val Loss: 0.4802 Accuracy: 0.8497\n",
      "Epoch: 39/150 Train Loss: 0.4283 Accuracy: 0.8633 Time: 15.84007  | Val Loss: 0.4487 Accuracy: 0.8589\n",
      "Epoch: 40/150 Train Loss: 0.4222 Accuracy: 0.8679 Time: 15.78456  | Val Loss: 0.4694 Accuracy: 0.8517\n",
      "Epoch: 41/150 Train Loss: 0.4210 Accuracy: 0.8624 Time: 15.81105  | Val Loss: 0.6716 Accuracy: 0.7987\n",
      "Epoch: 42/150 Train Loss: 0.4094 Accuracy: 0.8677 Time: 15.79770  | Val Loss: 0.5632 Accuracy: 0.8357\n",
      "Epoch: 43/150 Train Loss: 0.4062 Accuracy: 0.8682 Time: 15.81371  | Val Loss: 0.5091 Accuracy: 0.8390\n",
      "Epoch: 44/150 Train Loss: 0.4112 Accuracy: 0.8673 Time: 15.82313  | Val Loss: 0.6723 Accuracy: 0.8092\n",
      "Epoch: 45/150 Train Loss: 0.3751 Accuracy: 0.8775 Time: 15.80924  | Val Loss: 0.4413 Accuracy: 0.8642\n",
      "Epoch: 46/150 Train Loss: 0.3819 Accuracy: 0.8794 Time: 15.78079  | Val Loss: 0.4848 Accuracy: 0.8622\n",
      "Epoch: 47/150 Train Loss: 0.3874 Accuracy: 0.8763 Time: 15.75128  | Val Loss: 0.4568 Accuracy: 0.8604\n",
      "Epoch: 48/150 Train Loss: 0.3782 Accuracy: 0.8783 Time: 15.78336  | Val Loss: 0.5027 Accuracy: 0.8471\n",
      "Epoch: 49/150 Train Loss: 0.3806 Accuracy: 0.8758 Time: 15.80845  | Val Loss: 0.4646 Accuracy: 0.8550\n",
      "Epoch: 50/150 Train Loss: 0.3649 Accuracy: 0.8788 Time: 15.74537  | Val Loss: 0.4766 Accuracy: 0.8543\n",
      "Epoch: 51/150 Train Loss: 0.3605 Accuracy: 0.8846 Time: 15.78619  | Val Loss: 0.4289 Accuracy: 0.8645\n",
      "Epoch: 52/150 Train Loss: 0.3708 Accuracy: 0.8789 Time: 15.80295  | Val Loss: 0.4701 Accuracy: 0.8507\n",
      "Epoch: 53/150 Train Loss: 0.3593 Accuracy: 0.8847 Time: 15.81002  | Val Loss: 0.4013 Accuracy: 0.8685\n",
      "Epoch: 54/150 Train Loss: 0.3457 Accuracy: 0.8869 Time: 15.82205  | Val Loss: 0.4571 Accuracy: 0.8589\n",
      "Epoch: 55/150 Train Loss: 0.3488 Accuracy: 0.8864 Time: 15.80179  | Val Loss: 0.4365 Accuracy: 0.8652\n",
      "Epoch: 56/150 Train Loss: 0.3432 Accuracy: 0.8916 Time: 15.82998  | Val Loss: 0.4336 Accuracy: 0.8713\n",
      "Epoch: 57/150 Train Loss: 0.3273 Accuracy: 0.8907 Time: 15.80895  | Val Loss: 0.4083 Accuracy: 0.8718\n",
      "Epoch: 58/150 Train Loss: 0.3337 Accuracy: 0.8928 Time: 15.82770  | Val Loss: 0.4606 Accuracy: 0.8604\n",
      "Epoch: 59/150 Train Loss: 0.3196 Accuracy: 0.8940 Time: 15.89473  | Val Loss: 0.4418 Accuracy: 0.8619\n",
      "Epoch: 60/150 Train Loss: 0.3203 Accuracy: 0.8956 Time: 15.75463  | Val Loss: 0.4715 Accuracy: 0.8599\n",
      "Epoch: 61/150 Train Loss: 0.2739 Accuracy: 0.9097 Time: 15.82264  | Val Loss: 0.4175 Accuracy: 0.8688\n",
      "Epoch: 62/150 Train Loss: 0.2642 Accuracy: 0.9149 Time: 15.78521  | Val Loss: 0.3774 Accuracy: 0.8843\n",
      "Epoch: 63/150 Train Loss: 0.2501 Accuracy: 0.9187 Time: 15.76521  | Val Loss: 0.3734 Accuracy: 0.8871\n",
      "Epoch: 64/150 Train Loss: 0.2639 Accuracy: 0.9139 Time: 15.81635  | Val Loss: 0.3986 Accuracy: 0.8813\n",
      "Epoch: 65/150 Train Loss: 0.2363 Accuracy: 0.9220 Time: 15.82191  | Val Loss: 0.3842 Accuracy: 0.8803\n",
      "Epoch: 66/150 Train Loss: 0.2434 Accuracy: 0.9225 Time: 15.79718  | Val Loss: 0.3987 Accuracy: 0.8803\n",
      "Epoch: 67/150 Train Loss: 0.2395 Accuracy: 0.9222 Time: 15.76145  | Val Loss: 0.4012 Accuracy: 0.8805\n",
      "Epoch: 68/150 Train Loss: 0.2261 Accuracy: 0.9269 Time: 15.79547  | Val Loss: 0.4076 Accuracy: 0.8797\n",
      "Epoch: 69/150 Train Loss: 0.2286 Accuracy: 0.9235 Time: 15.81031  | Val Loss: 0.4082 Accuracy: 0.8780\n",
      "Epoch: 70/150 Train Loss: 0.2197 Accuracy: 0.9267 Time: 15.76415  | Val Loss: 0.4087 Accuracy: 0.8825\n",
      "Epoch: 71/150 Train Loss: 0.2247 Accuracy: 0.9271 Time: 15.79162  | Val Loss: 0.4097 Accuracy: 0.8828\n",
      "Epoch: 72/150 Train Loss: 0.2297 Accuracy: 0.9233 Time: 15.79817  | Val Loss: 0.3836 Accuracy: 0.8841\n",
      "Epoch: 73/150 Train Loss: 0.2297 Accuracy: 0.9255 Time: 15.81464  | Val Loss: 0.4100 Accuracy: 0.8800\n",
      "Epoch: 74/150 Train Loss: 0.2286 Accuracy: 0.9242 Time: 15.78630  | Val Loss: 0.3773 Accuracy: 0.8859\n",
      "Epoch: 75/150 Train Loss: 0.2173 Accuracy: 0.9291 Time: 15.82196  | Val Loss: 0.3974 Accuracy: 0.8887\n",
      "Epoch: 76/150 Train Loss: 0.2123 Accuracy: 0.9288 Time: 15.78407  | Val Loss: 0.3962 Accuracy: 0.8882\n",
      "Epoch: 77/150 Train Loss: 0.2051 Accuracy: 0.9319 Time: 15.78525  | Val Loss: 0.3947 Accuracy: 0.8841\n",
      "Epoch: 78/150 Train Loss: 0.2187 Accuracy: 0.9265 Time: 15.80984  | Val Loss: 0.4475 Accuracy: 0.8757\n",
      "Epoch: 79/150 Train Loss: 0.2062 Accuracy: 0.9337 Time: 15.76374  | Val Loss: 0.3941 Accuracy: 0.8864\n",
      "Epoch: 80/150 Train Loss: 0.2068 Accuracy: 0.9315 Time: 15.79505  | Val Loss: 0.3658 Accuracy: 0.8922\n",
      "Epoch: 81/150 Train Loss: 0.1903 Accuracy: 0.9403 Time: 15.77794  | Val Loss: 0.4558 Accuracy: 0.8680\n",
      "Epoch: 82/150 Train Loss: 0.2000 Accuracy: 0.9348 Time: 15.78712  | Val Loss: 0.3867 Accuracy: 0.8930\n",
      "Epoch: 83/150 Train Loss: 0.2064 Accuracy: 0.9328 Time: 15.77502  | Val Loss: 0.4580 Accuracy: 0.8690\n",
      "Epoch: 84/150 Train Loss: 0.1953 Accuracy: 0.9359 Time: 15.80185  | Val Loss: 0.4307 Accuracy: 0.8797\n",
      "Epoch: 85/150 Train Loss: 0.2037 Accuracy: 0.9354 Time: 15.81050  | Val Loss: 0.4208 Accuracy: 0.8828\n",
      "Epoch: 86/150 Train Loss: 0.1935 Accuracy: 0.9356 Time: 15.81388  | Val Loss: 0.4225 Accuracy: 0.8874\n",
      "Epoch: 87/150 Train Loss: 0.1907 Accuracy: 0.9373 Time: 15.78764  | Val Loss: 0.4018 Accuracy: 0.8925\n",
      "Epoch: 88/150 Train Loss: 0.1937 Accuracy: 0.9382 Time: 15.78128  | Val Loss: 0.3936 Accuracy: 0.8892\n",
      "Epoch: 89/150 Train Loss: 0.1772 Accuracy: 0.9416 Time: 15.79729  | Val Loss: 0.4539 Accuracy: 0.8767\n",
      "Epoch: 90/150 Train Loss: 0.1836 Accuracy: 0.9408 Time: 15.81767  | Val Loss: 0.3869 Accuracy: 0.8925\n",
      "Epoch: 91/150 Train Loss: 0.1666 Accuracy: 0.9461 Time: 15.81139  | Val Loss: 0.4027 Accuracy: 0.8884\n",
      "Epoch: 92/150 Train Loss: 0.1524 Accuracy: 0.9509 Time: 15.79474  | Val Loss: 0.3787 Accuracy: 0.8989\n",
      "Epoch: 93/150 Train Loss: 0.1512 Accuracy: 0.9516 Time: 15.76877  | Val Loss: 0.3820 Accuracy: 0.8932\n",
      "Epoch: 94/150 Train Loss: 0.1461 Accuracy: 0.9531 Time: 15.76592  | Val Loss: 0.3880 Accuracy: 0.8904\n",
      "Epoch: 95/150 Train Loss: 0.1461 Accuracy: 0.9534 Time: 15.75484  | Val Loss: 0.3994 Accuracy: 0.8910\n",
      "Epoch: 96/150 Train Loss: 0.1496 Accuracy: 0.9531 Time: 15.80778  | Val Loss: 0.4004 Accuracy: 0.8920\n",
      "Epoch: 97/150 Train Loss: 0.1425 Accuracy: 0.9538 Time: 15.77902  | Val Loss: 0.4009 Accuracy: 0.8907\n",
      "Epoch: 98/150 Train Loss: 0.1407 Accuracy: 0.9555 Time: 15.77694  | Val Loss: 0.3993 Accuracy: 0.8917\n",
      "Epoch: 99/150 Train Loss: 0.1375 Accuracy: 0.9550 Time: 15.82320  | Val Loss: 0.3755 Accuracy: 0.8991\n",
      "Epoch: 100/150 Train Loss: 0.1507 Accuracy: 0.9522 Time: 15.83163  | Val Loss: 0.4092 Accuracy: 0.8876\n",
      "Epoch: 101/150 Train Loss: 0.1453 Accuracy: 0.9542 Time: 15.79419  | Val Loss: 0.4028 Accuracy: 0.8925\n",
      "Epoch: 102/150 Train Loss: 0.1361 Accuracy: 0.9584 Time: 15.78630  | Val Loss: 0.4349 Accuracy: 0.8871\n",
      "Epoch: 103/150 Train Loss: 0.1305 Accuracy: 0.9572 Time: 15.77378  | Val Loss: 0.4116 Accuracy: 0.8935\n",
      "Epoch: 104/150 Train Loss: 0.1295 Accuracy: 0.9587 Time: 15.81981  | Val Loss: 0.4389 Accuracy: 0.8846\n",
      "Epoch: 105/150 Train Loss: 0.1292 Accuracy: 0.9591 Time: 15.78705  | Val Loss: 0.4269 Accuracy: 0.8912\n",
      "Epoch: 106/150 Train Loss: 0.1320 Accuracy: 0.9590 Time: 15.79063  | Val Loss: 0.3923 Accuracy: 0.8958\n",
      "Epoch: 107/150 Train Loss: 0.1237 Accuracy: 0.9598 Time: 15.78093  | Val Loss: 0.3989 Accuracy: 0.8953\n",
      "Epoch: 108/150 Train Loss: 0.1285 Accuracy: 0.9577 Time: 15.79823  | Val Loss: 0.4047 Accuracy: 0.8966\n",
      "Epoch: 109/150 Train Loss: 0.1274 Accuracy: 0.9606 Time: 15.77863  | Val Loss: 0.4244 Accuracy: 0.8874\n",
      "Epoch: 110/150 Train Loss: 0.1278 Accuracy: 0.9582 Time: 15.83719  | Val Loss: 0.4126 Accuracy: 0.8925\n",
      "Early stop at epoch 110!\n",
      "#Parameter: 23528522 Accuracy: 0.8922292993630573\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet50_config\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(128, train_dataset, val_dataset)\n",
    "net, resnet50_stats = train_net(\n",
    "    resnet50_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-4,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "990b5dc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/150 Train Loss: 2.2768 Accuracy: 0.2093 Time: 22.25189  | Val Loss: 1.9750 Accuracy: 0.2978\n",
      "Epoch: 2/150 Train Loss: 1.9252 Accuracy: 0.3164 Time: 21.88997  | Val Loss: 1.8041 Accuracy: 0.3613\n",
      "Epoch: 3/150 Train Loss: 1.7651 Accuracy: 0.4007 Time: 21.85791  | Val Loss: 1.7169 Accuracy: 0.3959\n",
      "Epoch: 4/150 Train Loss: 1.5691 Accuracy: 0.4754 Time: 21.85783  | Val Loss: 1.4929 Accuracy: 0.4899\n",
      "Epoch: 5/150 Train Loss: 1.4460 Accuracy: 0.5213 Time: 21.81843  | Val Loss: 1.3184 Accuracy: 0.5498\n",
      "Epoch: 6/150 Train Loss: 1.3004 Accuracy: 0.5723 Time: 21.79397  | Val Loss: 1.2944 Accuracy: 0.5929\n",
      "Epoch: 7/150 Train Loss: 1.2380 Accuracy: 0.5954 Time: 21.78773  | Val Loss: 1.1197 Accuracy: 0.6367\n",
      "Epoch: 8/150 Train Loss: 1.1545 Accuracy: 0.6297 Time: 21.87923  | Val Loss: 1.3805 Accuracy: 0.5857\n",
      "Epoch: 9/150 Train Loss: 1.0925 Accuracy: 0.6503 Time: 21.79546  | Val Loss: 0.9452 Accuracy: 0.6935\n",
      "Epoch: 10/150 Train Loss: 1.0161 Accuracy: 0.6784 Time: 21.84323  | Val Loss: 1.0783 Accuracy: 0.6464\n",
      "Epoch: 11/150 Train Loss: 0.9941 Accuracy: 0.6781 Time: 21.89944  | Val Loss: 1.0293 Accuracy: 0.6650\n",
      "Epoch: 12/150 Train Loss: 0.9457 Accuracy: 0.6981 Time: 21.90526  | Val Loss: 0.9629 Accuracy: 0.6978\n",
      "Epoch: 13/150 Train Loss: 0.9243 Accuracy: 0.7023 Time: 21.83763  | Val Loss: 0.9014 Accuracy: 0.7129\n",
      "Epoch: 14/150 Train Loss: 0.8912 Accuracy: 0.7169 Time: 21.80882  | Val Loss: 0.7576 Accuracy: 0.7564\n",
      "Epoch: 15/150 Train Loss: 0.8623 Accuracy: 0.7231 Time: 21.78837  | Val Loss: 0.7830 Accuracy: 0.7546\n",
      "Epoch: 16/150 Train Loss: 0.8227 Accuracy: 0.7358 Time: 21.89884  | Val Loss: 0.7206 Accuracy: 0.7755\n",
      "Epoch: 17/150 Train Loss: 0.8131 Accuracy: 0.7436 Time: 21.80306  | Val Loss: 0.8320 Accuracy: 0.7394\n",
      "Epoch: 18/150 Train Loss: 0.7905 Accuracy: 0.7455 Time: 21.79559  | Val Loss: 0.7820 Accuracy: 0.7419\n",
      "Epoch: 19/150 Train Loss: 0.7783 Accuracy: 0.7480 Time: 21.77217  | Val Loss: 0.6692 Accuracy: 0.7857\n",
      "Epoch: 20/150 Train Loss: 0.7580 Accuracy: 0.7582 Time: 21.80866  | Val Loss: 0.6754 Accuracy: 0.7817\n",
      "Epoch: 21/150 Train Loss: 0.7510 Accuracy: 0.7626 Time: 21.83676  | Val Loss: 0.6994 Accuracy: 0.7839\n",
      "Epoch: 22/150 Train Loss: 0.7243 Accuracy: 0.7682 Time: 21.85218  | Val Loss: 0.7306 Accuracy: 0.7722\n",
      "Epoch: 23/150 Train Loss: 0.7107 Accuracy: 0.7696 Time: 21.87961  | Val Loss: 0.9654 Accuracy: 0.7205\n",
      "Epoch: 24/150 Train Loss: 0.6863 Accuracy: 0.7798 Time: 21.86279  | Val Loss: 0.7930 Accuracy: 0.7656\n",
      "Epoch: 25/150 Train Loss: 0.6861 Accuracy: 0.7817 Time: 21.92112  | Val Loss: 0.7124 Accuracy: 0.7837\n",
      "Epoch: 26/150 Train Loss: 0.6762 Accuracy: 0.7884 Time: 21.86772  | Val Loss: 0.6295 Accuracy: 0.7959\n",
      "Epoch: 27/150 Train Loss: 0.6595 Accuracy: 0.7854 Time: 21.79269  | Val Loss: 0.6022 Accuracy: 0.8061\n",
      "Epoch: 28/150 Train Loss: 0.6569 Accuracy: 0.7902 Time: 21.83076  | Val Loss: 0.8160 Accuracy: 0.7544\n",
      "Epoch: 29/150 Train Loss: 0.6390 Accuracy: 0.7969 Time: 21.87883  | Val Loss: 0.6166 Accuracy: 0.8064\n",
      "Epoch: 30/150 Train Loss: 0.5999 Accuracy: 0.8152 Time: 21.80132  | Val Loss: 0.5608 Accuracy: 0.8275\n",
      "Epoch: 31/150 Train Loss: 0.5188 Accuracy: 0.8358 Time: 21.84304  | Val Loss: 0.4669 Accuracy: 0.8522\n",
      "Epoch: 32/150 Train Loss: 0.4873 Accuracy: 0.8428 Time: 21.86949  | Val Loss: 0.4477 Accuracy: 0.8614\n",
      "Epoch: 33/150 Train Loss: 0.4942 Accuracy: 0.8400 Time: 21.80056  | Val Loss: 0.4672 Accuracy: 0.8530\n",
      "Epoch: 34/150 Train Loss: 0.4695 Accuracy: 0.8468 Time: 21.79763  | Val Loss: 0.4528 Accuracy: 0.8599\n",
      "Epoch: 35/150 Train Loss: 0.4794 Accuracy: 0.8471 Time: 21.80176  | Val Loss: 0.4970 Accuracy: 0.8436\n",
      "Epoch: 36/150 Train Loss: 0.4678 Accuracy: 0.8502 Time: 21.81984  | Val Loss: 0.4644 Accuracy: 0.8540\n",
      "Epoch: 37/150 Train Loss: 0.4554 Accuracy: 0.8506 Time: 21.84901  | Val Loss: 0.4569 Accuracy: 0.8576\n",
      "Epoch: 38/150 Train Loss: 0.4539 Accuracy: 0.8533 Time: 21.85126  | Val Loss: 0.4373 Accuracy: 0.8647\n",
      "Epoch: 39/150 Train Loss: 0.4446 Accuracy: 0.8535 Time: 21.93420  | Val Loss: 0.4865 Accuracy: 0.8497\n",
      "Epoch: 40/150 Train Loss: 0.4293 Accuracy: 0.8661 Time: 21.92349  | Val Loss: 0.5419 Accuracy: 0.8448\n",
      "Epoch: 41/150 Train Loss: 0.4173 Accuracy: 0.8680 Time: 21.87956  | Val Loss: 0.4475 Accuracy: 0.8619\n",
      "Epoch: 42/150 Train Loss: 0.4243 Accuracy: 0.8660 Time: 21.89567  | Val Loss: 0.4417 Accuracy: 0.8619\n",
      "Epoch: 43/150 Train Loss: 0.4256 Accuracy: 0.8625 Time: 21.83068  | Val Loss: 0.4415 Accuracy: 0.8670\n",
      "Epoch: 44/150 Train Loss: 0.3990 Accuracy: 0.8699 Time: 21.82141  | Val Loss: 0.4753 Accuracy: 0.8540\n",
      "Epoch: 45/150 Train Loss: 0.4099 Accuracy: 0.8701 Time: 21.92391  | Val Loss: 0.4217 Accuracy: 0.8721\n",
      "Epoch: 46/150 Train Loss: 0.3965 Accuracy: 0.8686 Time: 21.85888  | Val Loss: 0.4557 Accuracy: 0.8558\n",
      "Epoch: 47/150 Train Loss: 0.3959 Accuracy: 0.8737 Time: 21.82727  | Val Loss: 0.4923 Accuracy: 0.8487\n",
      "Epoch: 48/150 Train Loss: 0.3952 Accuracy: 0.8716 Time: 21.81155  | Val Loss: 0.4545 Accuracy: 0.8591\n",
      "Epoch: 49/150 Train Loss: 0.3690 Accuracy: 0.8825 Time: 21.80127  | Val Loss: 0.4069 Accuracy: 0.8645\n",
      "Epoch: 50/150 Train Loss: 0.3716 Accuracy: 0.8795 Time: 21.88891  | Val Loss: 0.5232 Accuracy: 0.8443\n",
      "Epoch: 51/150 Train Loss: 0.3798 Accuracy: 0.8793 Time: 21.87248  | Val Loss: 0.4335 Accuracy: 0.8665\n",
      "Epoch: 52/150 Train Loss: 0.3557 Accuracy: 0.8837 Time: 21.89556  | Val Loss: 0.4299 Accuracy: 0.8693\n",
      "Epoch: 53/150 Train Loss: 0.3662 Accuracy: 0.8807 Time: 21.80707  | Val Loss: 0.4424 Accuracy: 0.8673\n",
      "Epoch: 54/150 Train Loss: 0.3491 Accuracy: 0.8860 Time: 21.86604  | Val Loss: 0.4392 Accuracy: 0.8601\n",
      "Epoch: 55/150 Train Loss: 0.3470 Accuracy: 0.8893 Time: 21.84894  | Val Loss: 0.4554 Accuracy: 0.8586\n",
      "Epoch: 56/150 Train Loss: 0.3510 Accuracy: 0.8871 Time: 21.86152  | Val Loss: 0.4671 Accuracy: 0.8586\n",
      "Epoch: 57/150 Train Loss: 0.3561 Accuracy: 0.8849 Time: 21.82301  | Val Loss: 0.4271 Accuracy: 0.8729\n",
      "Epoch: 58/150 Train Loss: 0.3418 Accuracy: 0.8882 Time: 21.86855  | Val Loss: 0.4099 Accuracy: 0.8769\n",
      "Epoch: 59/150 Train Loss: 0.3554 Accuracy: 0.8862 Time: 21.91050  | Val Loss: 0.3979 Accuracy: 0.8744\n",
      "Epoch: 60/150 Train Loss: 0.3270 Accuracy: 0.8953 Time: 21.82439  | Val Loss: 0.4244 Accuracy: 0.8741\n",
      "Epoch: 61/150 Train Loss: 0.2763 Accuracy: 0.9120 Time: 21.83575  | Val Loss: 0.3871 Accuracy: 0.8828\n",
      "Epoch: 62/150 Train Loss: 0.2590 Accuracy: 0.9174 Time: 21.79127  | Val Loss: 0.4136 Accuracy: 0.8800\n",
      "Epoch: 63/150 Train Loss: 0.2632 Accuracy: 0.9144 Time: 21.81740  | Val Loss: 0.3810 Accuracy: 0.8869\n",
      "Epoch: 64/150 Train Loss: 0.2540 Accuracy: 0.9169 Time: 21.82306  | Val Loss: 0.4182 Accuracy: 0.8759\n",
      "Epoch: 65/150 Train Loss: 0.2444 Accuracy: 0.9208 Time: 21.81720  | Val Loss: 0.4204 Accuracy: 0.8797\n",
      "Epoch: 66/150 Train Loss: 0.2442 Accuracy: 0.9190 Time: 21.83495  | Val Loss: 0.4186 Accuracy: 0.8777\n",
      "Epoch: 67/150 Train Loss: 0.2389 Accuracy: 0.9228 Time: 21.79867  | Val Loss: 0.3924 Accuracy: 0.8871\n",
      "Epoch: 68/150 Train Loss: 0.2426 Accuracy: 0.9249 Time: 21.79693  | Val Loss: 0.4002 Accuracy: 0.8818\n",
      "Epoch: 69/150 Train Loss: 0.2335 Accuracy: 0.9236 Time: 22.01371  | Val Loss: 0.3828 Accuracy: 0.8902\n",
      "Epoch: 70/150 Train Loss: 0.2382 Accuracy: 0.9212 Time: 21.85564  | Val Loss: 0.3804 Accuracy: 0.8882\n",
      "Epoch: 71/150 Train Loss: 0.2321 Accuracy: 0.9244 Time: 21.86170  | Val Loss: 0.3956 Accuracy: 0.8876\n",
      "Epoch: 72/150 Train Loss: 0.2284 Accuracy: 0.9254 Time: 21.87133  | Val Loss: 0.3882 Accuracy: 0.8838\n",
      "Epoch: 73/150 Train Loss: 0.2256 Accuracy: 0.9261 Time: 21.86120  | Val Loss: 0.3952 Accuracy: 0.8899\n",
      "Epoch: 74/150 Train Loss: 0.2237 Accuracy: 0.9273 Time: 21.95615  | Val Loss: 0.4157 Accuracy: 0.8838\n",
      "Epoch: 75/150 Train Loss: 0.2232 Accuracy: 0.9282 Time: 21.79400  | Val Loss: 0.3795 Accuracy: 0.8871\n",
      "Epoch: 76/150 Train Loss: 0.2247 Accuracy: 0.9264 Time: 21.82110  | Val Loss: 0.3787 Accuracy: 0.8922\n",
      "Epoch: 77/150 Train Loss: 0.2233 Accuracy: 0.9261 Time: 21.81109  | Val Loss: 0.3773 Accuracy: 0.8907\n",
      "Epoch: 78/150 Train Loss: 0.2131 Accuracy: 0.9317 Time: 21.81188  | Val Loss: 0.3672 Accuracy: 0.8943\n",
      "Epoch: 79/150 Train Loss: 0.2157 Accuracy: 0.9282 Time: 21.85271  | Val Loss: 0.3911 Accuracy: 0.8884\n",
      "Epoch: 80/150 Train Loss: 0.2305 Accuracy: 0.9253 Time: 21.85793  | Val Loss: 0.3691 Accuracy: 0.8955\n",
      "Epoch: 81/150 Train Loss: 0.2112 Accuracy: 0.9330 Time: 21.82772  | Val Loss: 0.3971 Accuracy: 0.8876\n",
      "Epoch: 82/150 Train Loss: 0.2031 Accuracy: 0.9339 Time: 21.89549  | Val Loss: 0.4230 Accuracy: 0.8856\n",
      "Epoch: 83/150 Train Loss: 0.2007 Accuracy: 0.9336 Time: 22.01474  | Val Loss: 0.4056 Accuracy: 0.8917\n",
      "Epoch: 84/150 Train Loss: 0.2037 Accuracy: 0.9330 Time: 21.86870  | Val Loss: 0.4265 Accuracy: 0.8808\n",
      "Epoch: 85/150 Train Loss: 0.1970 Accuracy: 0.9353 Time: 21.85090  | Val Loss: 0.3762 Accuracy: 0.8938\n",
      "Epoch: 86/150 Train Loss: 0.1812 Accuracy: 0.9397 Time: 21.86593  | Val Loss: 0.4156 Accuracy: 0.8917\n",
      "Epoch: 87/150 Train Loss: 0.1952 Accuracy: 0.9360 Time: 21.90040  | Val Loss: 0.3925 Accuracy: 0.8897\n",
      "Epoch: 88/150 Train Loss: 0.1980 Accuracy: 0.9362 Time: 21.89775  | Val Loss: 0.3828 Accuracy: 0.8915\n",
      "Epoch: 89/150 Train Loss: 0.1980 Accuracy: 0.9358 Time: 21.84489  | Val Loss: 0.4051 Accuracy: 0.8856\n",
      "Epoch: 90/150 Train Loss: 0.1728 Accuracy: 0.9451 Time: 21.80957  | Val Loss: 0.4124 Accuracy: 0.8887\n",
      "Epoch: 91/150 Train Loss: 0.1694 Accuracy: 0.9453 Time: 21.80861  | Val Loss: 0.3858 Accuracy: 0.8950\n",
      "Epoch: 92/150 Train Loss: 0.1674 Accuracy: 0.9446 Time: 21.94394  | Val Loss: 0.3736 Accuracy: 0.8989\n",
      "Epoch: 93/150 Train Loss: 0.1480 Accuracy: 0.9505 Time: 21.83965  | Val Loss: 0.3846 Accuracy: 0.8948\n",
      "Epoch: 94/150 Train Loss: 0.1531 Accuracy: 0.9517 Time: 21.80593  | Val Loss: 0.3895 Accuracy: 0.8953\n",
      "Epoch: 95/150 Train Loss: 0.1538 Accuracy: 0.9507 Time: 21.82960  | Val Loss: 0.3905 Accuracy: 0.8971\n",
      "Epoch: 96/150 Train Loss: 0.1512 Accuracy: 0.9486 Time: 21.82646  | Val Loss: 0.3820 Accuracy: 0.8971\n",
      "Epoch: 97/150 Train Loss: 0.1409 Accuracy: 0.9581 Time: 21.81465  | Val Loss: 0.3701 Accuracy: 0.8958\n",
      "Epoch: 98/150 Train Loss: 0.1531 Accuracy: 0.9488 Time: 21.84292  | Val Loss: 0.3874 Accuracy: 0.8989\n",
      "Epoch: 99/150 Train Loss: 0.1355 Accuracy: 0.9549 Time: 21.76322  | Val Loss: 0.3745 Accuracy: 0.9022\n",
      "Epoch: 100/150 Train Loss: 0.1412 Accuracy: 0.9561 Time: 21.88667  | Val Loss: 0.3951 Accuracy: 0.8986\n",
      "Epoch: 101/150 Train Loss: 0.1428 Accuracy: 0.9531 Time: 21.86421  | Val Loss: 0.4004 Accuracy: 0.8968\n",
      "Epoch: 102/150 Train Loss: 0.1330 Accuracy: 0.9569 Time: 21.90699  | Val Loss: 0.4105 Accuracy: 0.8938\n",
      "Epoch: 103/150 Train Loss: 0.1419 Accuracy: 0.9523 Time: 21.89232  | Val Loss: 0.3966 Accuracy: 0.8978\n",
      "Epoch: 104/150 Train Loss: 0.1335 Accuracy: 0.9553 Time: 21.83398  | Val Loss: 0.3909 Accuracy: 0.9001\n",
      "Epoch: 105/150 Train Loss: 0.1396 Accuracy: 0.9559 Time: 21.85001  | Val Loss: 0.3874 Accuracy: 0.8948\n",
      "Epoch: 106/150 Train Loss: 0.1350 Accuracy: 0.9567 Time: 21.84346  | Val Loss: 0.3994 Accuracy: 0.8935\n",
      "Epoch: 107/150 Train Loss: 0.1283 Accuracy: 0.9598 Time: 21.86995  | Val Loss: 0.3949 Accuracy: 0.8961\n",
      "Epoch: 108/150 Train Loss: 0.1321 Accuracy: 0.9573 Time: 21.88436  | Val Loss: 0.4178 Accuracy: 0.8966\n",
      "Early stop at epoch 108!\n",
      "#Parameter: 42520650 Accuracy: 0.8942675159235669\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet101_config\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(64, train_dataset, val_dataset)\n",
    "net, resnet101_stats = train_net(\n",
    "    resnet101_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-4,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e15d541c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/150 Train Loss: 2.3874 Accuracy: 0.1823 Time: 29.39404  | Val Loss: 2.0808 Accuracy: 0.2224\n",
      "Epoch: 2/150 Train Loss: 2.0540 Accuracy: 0.2553 Time: 29.43147  | Val Loss: 1.8572 Accuracy: 0.3297\n",
      "Epoch: 3/150 Train Loss: 1.8562 Accuracy: 0.3443 Time: 29.51494  | Val Loss: 1.8000 Accuracy: 0.3572\n",
      "Epoch: 4/150 Train Loss: 1.7521 Accuracy: 0.3837 Time: 29.46161  | Val Loss: 1.8238 Accuracy: 0.3811\n",
      "Epoch: 5/150 Train Loss: 1.6573 Accuracy: 0.4258 Time: 29.51271  | Val Loss: 1.7863 Accuracy: 0.3842\n",
      "Epoch: 6/150 Train Loss: 1.5714 Accuracy: 0.4678 Time: 29.45594  | Val Loss: 1.3881 Accuracy: 0.5417\n",
      "Epoch: 7/150 Train Loss: 1.4538 Accuracy: 0.5159 Time: 29.42910  | Val Loss: 1.5601 Accuracy: 0.4894\n",
      "Epoch: 8/150 Train Loss: 1.3833 Accuracy: 0.5438 Time: 29.42076  | Val Loss: 1.2751 Accuracy: 0.5918\n",
      "Epoch: 9/150 Train Loss: 1.2972 Accuracy: 0.5774 Time: 29.44181  | Val Loss: 1.0998 Accuracy: 0.6487\n",
      "Epoch: 10/150 Train Loss: 1.2330 Accuracy: 0.5971 Time: 29.43030  | Val Loss: 1.1870 Accuracy: 0.6273\n",
      "Epoch: 11/150 Train Loss: 1.1612 Accuracy: 0.6246 Time: 29.43990  | Val Loss: 1.3608 Accuracy: 0.5666\n",
      "Epoch: 12/150 Train Loss: 1.1050 Accuracy: 0.6429 Time: 29.41383  | Val Loss: 1.0358 Accuracy: 0.6540\n",
      "Epoch: 13/150 Train Loss: 1.0761 Accuracy: 0.6520 Time: 29.42491  | Val Loss: 0.9510 Accuracy: 0.6943\n",
      "Epoch: 14/150 Train Loss: 1.0174 Accuracy: 0.6734 Time: 29.39951  | Val Loss: 0.8909 Accuracy: 0.7175\n",
      "Epoch: 15/150 Train Loss: 0.9757 Accuracy: 0.6912 Time: 29.43677  | Val Loss: 0.9745 Accuracy: 0.6940\n",
      "Epoch: 16/150 Train Loss: 0.9285 Accuracy: 0.6983 Time: 29.48758  | Val Loss: 0.9374 Accuracy: 0.7085\n",
      "Epoch: 17/150 Train Loss: 0.9163 Accuracy: 0.7124 Time: 29.46575  | Val Loss: 0.8361 Accuracy: 0.7261\n",
      "Epoch: 18/150 Train Loss: 0.8635 Accuracy: 0.7300 Time: 29.46808  | Val Loss: 0.8004 Accuracy: 0.7355\n",
      "Epoch: 19/150 Train Loss: 0.8530 Accuracy: 0.7306 Time: 29.47424  | Val Loss: 0.8795 Accuracy: 0.7195\n",
      "Epoch: 20/150 Train Loss: 0.8185 Accuracy: 0.7396 Time: 29.46033  | Val Loss: 0.7127 Accuracy: 0.7697\n",
      "Epoch: 21/150 Train Loss: 0.8152 Accuracy: 0.7415 Time: 29.45849  | Val Loss: 1.0689 Accuracy: 0.6833\n",
      "Epoch: 22/150 Train Loss: 0.8076 Accuracy: 0.7449 Time: 29.46987  | Val Loss: 0.7621 Accuracy: 0.7643\n",
      "Epoch: 23/150 Train Loss: 0.7717 Accuracy: 0.7578 Time: 29.40239  | Val Loss: 1.0953 Accuracy: 0.6955\n",
      "Epoch: 24/150 Train Loss: 0.7569 Accuracy: 0.7625 Time: 29.43702  | Val Loss: 0.7309 Accuracy: 0.7753\n",
      "Epoch: 25/150 Train Loss: 0.7434 Accuracy: 0.7666 Time: 29.45951  | Val Loss: 0.9516 Accuracy: 0.7134\n",
      "Epoch: 26/150 Train Loss: 0.7375 Accuracy: 0.7680 Time: 29.47995  | Val Loss: 0.6366 Accuracy: 0.7957\n",
      "Epoch: 27/150 Train Loss: 0.7105 Accuracy: 0.7728 Time: 29.47077  | Val Loss: 0.6779 Accuracy: 0.7898\n",
      "Epoch: 28/150 Train Loss: 0.6980 Accuracy: 0.7781 Time: 29.48399  | Val Loss: 0.5946 Accuracy: 0.8214\n",
      "Epoch: 29/150 Train Loss: 0.6772 Accuracy: 0.7858 Time: 29.43109  | Val Loss: 0.6615 Accuracy: 0.7865\n",
      "Epoch: 30/150 Train Loss: 0.6909 Accuracy: 0.7862 Time: 29.45360  | Val Loss: 0.5847 Accuracy: 0.8214\n",
      "Epoch: 31/150 Train Loss: 0.5866 Accuracy: 0.8194 Time: 29.45952  | Val Loss: 0.5173 Accuracy: 0.8408\n",
      "Epoch: 32/150 Train Loss: 0.5328 Accuracy: 0.8322 Time: 29.47225  | Val Loss: 0.4703 Accuracy: 0.8517\n",
      "Epoch: 33/150 Train Loss: 0.5312 Accuracy: 0.8309 Time: 29.45372  | Val Loss: 0.4940 Accuracy: 0.8382\n",
      "Epoch: 34/150 Train Loss: 0.5225 Accuracy: 0.8327 Time: 29.41971  | Val Loss: 0.5961 Accuracy: 0.8255\n",
      "Epoch: 35/150 Train Loss: 0.5229 Accuracy: 0.8339 Time: 29.39927  | Val Loss: 0.6385 Accuracy: 0.8120\n",
      "Epoch: 36/150 Train Loss: 0.5165 Accuracy: 0.8396 Time: 29.46492  | Val Loss: 0.4597 Accuracy: 0.8540\n",
      "Epoch: 37/150 Train Loss: 0.5104 Accuracy: 0.8414 Time: 29.43270  | Val Loss: 0.5484 Accuracy: 0.8380\n",
      "Epoch: 38/150 Train Loss: 0.4961 Accuracy: 0.8455 Time: 29.50209  | Val Loss: 0.4637 Accuracy: 0.8583\n",
      "Epoch: 39/150 Train Loss: 0.5029 Accuracy: 0.8365 Time: 29.48883  | Val Loss: 0.4502 Accuracy: 0.8601\n",
      "Epoch: 40/150 Train Loss: 0.4874 Accuracy: 0.8435 Time: 29.42139  | Val Loss: 0.5129 Accuracy: 0.8423\n",
      "Epoch: 41/150 Train Loss: 0.4658 Accuracy: 0.8499 Time: 29.48094  | Val Loss: 0.4323 Accuracy: 0.8637\n",
      "Epoch: 42/150 Train Loss: 0.4767 Accuracy: 0.8492 Time: 29.42439  | Val Loss: 0.4399 Accuracy: 0.8665\n",
      "Epoch: 43/150 Train Loss: 0.4581 Accuracy: 0.8505 Time: 29.47063  | Val Loss: 0.4686 Accuracy: 0.8571\n",
      "Epoch: 44/150 Train Loss: 0.4532 Accuracy: 0.8539 Time: 29.44427  | Val Loss: 0.4712 Accuracy: 0.8568\n",
      "Epoch: 45/150 Train Loss: 0.4448 Accuracy: 0.8552 Time: 29.39847  | Val Loss: 0.4616 Accuracy: 0.8601\n",
      "Epoch: 46/150 Train Loss: 0.4376 Accuracy: 0.8606 Time: 29.40424  | Val Loss: 0.4570 Accuracy: 0.8599\n",
      "Epoch: 47/150 Train Loss: 0.4410 Accuracy: 0.8576 Time: 29.40436  | Val Loss: 0.4430 Accuracy: 0.8606\n",
      "Epoch: 48/150 Train Loss: 0.4237 Accuracy: 0.8647 Time: 29.49180  | Val Loss: 0.4533 Accuracy: 0.8601\n",
      "Epoch: 49/150 Train Loss: 0.4138 Accuracy: 0.8682 Time: 29.43074  | Val Loss: 0.4262 Accuracy: 0.8678\n",
      "Epoch: 50/150 Train Loss: 0.4266 Accuracy: 0.8661 Time: 29.42332  | Val Loss: 0.4112 Accuracy: 0.8734\n",
      "Epoch: 51/150 Train Loss: 0.4231 Accuracy: 0.8652 Time: 29.51808  | Val Loss: 0.4154 Accuracy: 0.8772\n",
      "Epoch: 52/150 Train Loss: 0.3937 Accuracy: 0.8750 Time: 29.43618  | Val Loss: 0.4361 Accuracy: 0.8685\n",
      "Epoch: 53/150 Train Loss: 0.3946 Accuracy: 0.8750 Time: 29.41994  | Val Loss: 0.4131 Accuracy: 0.8724\n",
      "Epoch: 54/150 Train Loss: 0.3960 Accuracy: 0.8716 Time: 29.47139  | Val Loss: 0.5073 Accuracy: 0.8504\n",
      "Epoch: 55/150 Train Loss: 0.3824 Accuracy: 0.8791 Time: 29.45174  | Val Loss: 0.4398 Accuracy: 0.8708\n",
      "Epoch: 56/150 Train Loss: 0.3750 Accuracy: 0.8800 Time: 29.47414  | Val Loss: 0.5042 Accuracy: 0.8601\n",
      "Epoch: 57/150 Train Loss: 0.3979 Accuracy: 0.8730 Time: 29.45362  | Val Loss: 0.4277 Accuracy: 0.8632\n",
      "Epoch: 58/150 Train Loss: 0.3778 Accuracy: 0.8791 Time: 29.45352  | Val Loss: 0.4358 Accuracy: 0.8678\n",
      "Epoch: 59/150 Train Loss: 0.3629 Accuracy: 0.8834 Time: 29.45438  | Val Loss: 0.4300 Accuracy: 0.8716\n",
      "Epoch: 60/150 Train Loss: 0.3679 Accuracy: 0.8803 Time: 29.43170  | Val Loss: 0.4649 Accuracy: 0.8614\n",
      "Epoch: 61/150 Train Loss: 0.3257 Accuracy: 0.8939 Time: 29.42439  | Val Loss: 0.3740 Accuracy: 0.8854\n",
      "Epoch: 62/150 Train Loss: 0.2914 Accuracy: 0.9047 Time: 29.44054  | Val Loss: 0.3818 Accuracy: 0.8843\n",
      "Epoch: 63/150 Train Loss: 0.2879 Accuracy: 0.9071 Time: 29.42633  | Val Loss: 0.3870 Accuracy: 0.8859\n",
      "Epoch: 64/150 Train Loss: 0.2899 Accuracy: 0.9045 Time: 29.46135  | Val Loss: 0.3811 Accuracy: 0.8854\n",
      "Epoch: 65/150 Train Loss: 0.2869 Accuracy: 0.9079 Time: 29.46407  | Val Loss: 0.3493 Accuracy: 0.8950\n",
      "Epoch: 66/150 Train Loss: 0.2790 Accuracy: 0.9097 Time: 29.45391  | Val Loss: 0.3727 Accuracy: 0.8861\n",
      "Epoch: 67/150 Train Loss: 0.2832 Accuracy: 0.9076 Time: 29.46000  | Val Loss: 0.3679 Accuracy: 0.8938\n",
      "Epoch: 68/150 Train Loss: 0.2736 Accuracy: 0.9136 Time: 29.41161  | Val Loss: 0.3704 Accuracy: 0.8884\n",
      "Epoch: 69/150 Train Loss: 0.2552 Accuracy: 0.9157 Time: 29.42757  | Val Loss: 0.3954 Accuracy: 0.8864\n",
      "Epoch: 70/150 Train Loss: 0.2552 Accuracy: 0.9176 Time: 29.44157  | Val Loss: 0.3977 Accuracy: 0.8841\n",
      "Epoch: 71/150 Train Loss: 0.2676 Accuracy: 0.9142 Time: 29.46216  | Val Loss: 0.4056 Accuracy: 0.8815\n",
      "Epoch: 72/150 Train Loss: 0.2607 Accuracy: 0.9158 Time: 29.44119  | Val Loss: 0.4152 Accuracy: 0.8831\n",
      "Epoch: 73/150 Train Loss: 0.2708 Accuracy: 0.9126 Time: 29.41560  | Val Loss: 0.3594 Accuracy: 0.8892\n",
      "Epoch: 74/150 Train Loss: 0.2394 Accuracy: 0.9206 Time: 29.44006  | Val Loss: 0.3602 Accuracy: 0.8950\n",
      "Epoch: 75/150 Train Loss: 0.2648 Accuracy: 0.9169 Time: 29.43172  | Val Loss: 0.3905 Accuracy: 0.8841\n",
      "Epoch: 76/150 Train Loss: 0.2489 Accuracy: 0.9189 Time: 29.42156  | Val Loss: 0.3929 Accuracy: 0.8851\n",
      "Epoch: 77/150 Train Loss: 0.2346 Accuracy: 0.9266 Time: 29.48369  | Val Loss: 0.3778 Accuracy: 0.8910\n",
      "Epoch: 78/150 Train Loss: 0.2395 Accuracy: 0.9241 Time: 29.44419  | Val Loss: 0.3786 Accuracy: 0.8922\n",
      "Epoch: 79/150 Train Loss: 0.2467 Accuracy: 0.9210 Time: 29.45804  | Val Loss: 0.4351 Accuracy: 0.8716\n",
      "Epoch: 80/150 Train Loss: 0.2469 Accuracy: 0.9221 Time: 29.45065  | Val Loss: 0.3897 Accuracy: 0.8904\n",
      "Epoch: 81/150 Train Loss: 0.2354 Accuracy: 0.9248 Time: 29.43779  | Val Loss: 0.3928 Accuracy: 0.8879\n",
      "Epoch: 82/150 Train Loss: 0.2360 Accuracy: 0.9229 Time: 29.43862  | Val Loss: 0.4465 Accuracy: 0.8797\n",
      "Epoch: 83/150 Train Loss: 0.2223 Accuracy: 0.9279 Time: 29.45240  | Val Loss: 0.4056 Accuracy: 0.8846\n",
      "Epoch: 84/150 Train Loss: 0.2266 Accuracy: 0.9260 Time: 29.45007  | Val Loss: 0.3735 Accuracy: 0.8938\n",
      "Epoch: 85/150 Train Loss: 0.2238 Accuracy: 0.9266 Time: 29.45422  | Val Loss: 0.3811 Accuracy: 0.8920\n",
      "Epoch: 86/150 Train Loss: 0.2079 Accuracy: 0.9335 Time: 29.41119  | Val Loss: 0.4067 Accuracy: 0.8869\n",
      "Epoch: 87/150 Train Loss: 0.2240 Accuracy: 0.9277 Time: 29.41519  | Val Loss: 0.3796 Accuracy: 0.8943\n",
      "Epoch: 88/150 Train Loss: 0.2106 Accuracy: 0.9320 Time: 29.44800  | Val Loss: 0.4161 Accuracy: 0.8869\n",
      "Epoch: 89/150 Train Loss: 0.2247 Accuracy: 0.9271 Time: 29.43494  | Val Loss: 0.3748 Accuracy: 0.8938\n",
      "Epoch: 90/150 Train Loss: 0.2240 Accuracy: 0.9290 Time: 29.42524  | Val Loss: 0.3863 Accuracy: 0.8889\n",
      "Epoch: 91/150 Train Loss: 0.1811 Accuracy: 0.9409 Time: 29.45777  | Val Loss: 0.3797 Accuracy: 0.8950\n",
      "Epoch: 92/150 Train Loss: 0.1805 Accuracy: 0.9418 Time: 29.42841  | Val Loss: 0.3449 Accuracy: 0.9055\n",
      "Epoch: 93/150 Train Loss: 0.1715 Accuracy: 0.9472 Time: 29.49388  | Val Loss: 0.3540 Accuracy: 0.9039\n",
      "Epoch: 94/150 Train Loss: 0.1679 Accuracy: 0.9488 Time: 29.45302  | Val Loss: 0.3722 Accuracy: 0.8983\n",
      "Epoch: 95/150 Train Loss: 0.1720 Accuracy: 0.9415 Time: 29.48039  | Val Loss: 0.3568 Accuracy: 0.9042\n",
      "Epoch: 96/150 Train Loss: 0.1625 Accuracy: 0.9490 Time: 29.46241  | Val Loss: 0.3686 Accuracy: 0.8999\n",
      "Epoch: 97/150 Train Loss: 0.1737 Accuracy: 0.9438 Time: 29.45462  | Val Loss: 0.3682 Accuracy: 0.9029\n",
      "Epoch: 98/150 Train Loss: 0.1593 Accuracy: 0.9497 Time: 29.42471  | Val Loss: 0.3735 Accuracy: 0.9039\n",
      "Epoch: 99/150 Train Loss: 0.1609 Accuracy: 0.9462 Time: 29.49964  | Val Loss: 0.3632 Accuracy: 0.9011\n",
      "Epoch: 100/150 Train Loss: 0.1647 Accuracy: 0.9474 Time: 29.45863  | Val Loss: 0.3781 Accuracy: 0.8999\n",
      "Epoch: 101/150 Train Loss: 0.1552 Accuracy: 0.9489 Time: 29.43751  | Val Loss: 0.3710 Accuracy: 0.9014\n",
      "Epoch: 102/150 Train Loss: 0.1470 Accuracy: 0.9521 Time: 29.45764  | Val Loss: 0.3704 Accuracy: 0.9017\n",
      "Epoch: 103/150 Train Loss: 0.1564 Accuracy: 0.9513 Time: 29.42460  | Val Loss: 0.3754 Accuracy: 0.8986\n",
      "Epoch: 104/150 Train Loss: 0.1536 Accuracy: 0.9522 Time: 29.44632  | Val Loss: 0.3658 Accuracy: 0.9027\n",
      "Epoch: 105/150 Train Loss: 0.1584 Accuracy: 0.9471 Time: 29.40864  | Val Loss: 0.3769 Accuracy: 0.9006\n",
      "Epoch: 106/150 Train Loss: 0.1576 Accuracy: 0.9487 Time: 29.43441  | Val Loss: 0.3890 Accuracy: 0.8983\n",
      "Epoch: 107/150 Train Loss: 0.1541 Accuracy: 0.9506 Time: 29.44419  | Val Loss: 0.3555 Accuracy: 0.9034\n",
      "Epoch: 108/150 Train Loss: 0.1451 Accuracy: 0.9526 Time: 29.46674  | Val Loss: 0.4021 Accuracy: 0.8996\n",
      "Epoch: 109/150 Train Loss: 0.1413 Accuracy: 0.9524 Time: 29.42144  | Val Loss: 0.4064 Accuracy: 0.9001\n",
      "Epoch: 110/150 Train Loss: 0.1391 Accuracy: 0.9546 Time: 29.47128  | Val Loss: 0.3897 Accuracy: 0.9004\n",
      "Epoch: 111/150 Train Loss: 0.1643 Accuracy: 0.9496 Time: 29.46560  | Val Loss: 0.3782 Accuracy: 0.8983\n",
      "Epoch: 112/150 Train Loss: 0.1504 Accuracy: 0.9511 Time: 29.43511  | Val Loss: 0.3952 Accuracy: 0.8968\n",
      "Epoch: 113/150 Train Loss: 0.1407 Accuracy: 0.9547 Time: 29.42490  | Val Loss: 0.3624 Accuracy: 0.9045\n",
      "Epoch: 114/150 Train Loss: 0.1297 Accuracy: 0.9570 Time: 29.44213  | Val Loss: 0.3923 Accuracy: 0.9009\n",
      "Epoch: 115/150 Train Loss: 0.1377 Accuracy: 0.9546 Time: 29.44860  | Val Loss: 0.3940 Accuracy: 0.9001\n",
      "Epoch: 116/150 Train Loss: 0.1356 Accuracy: 0.9537 Time: 29.47358  | Val Loss: 0.3886 Accuracy: 0.9009\n",
      "Epoch: 117/150 Train Loss: 0.1460 Accuracy: 0.9527 Time: 29.41241  | Val Loss: 0.3796 Accuracy: 0.8961\n",
      "Epoch: 118/150 Train Loss: 0.1497 Accuracy: 0.9528 Time: 29.43103  | Val Loss: 0.3841 Accuracy: 0.9001\n",
      "Epoch: 119/150 Train Loss: 0.1305 Accuracy: 0.9603 Time: 29.44215  | Val Loss: 0.3818 Accuracy: 0.9062\n",
      "Epoch: 120/150 Train Loss: 0.1274 Accuracy: 0.9588 Time: 29.44596  | Val Loss: 0.4168 Accuracy: 0.8961\n",
      "Epoch: 121/150 Train Loss: 0.1212 Accuracy: 0.9593 Time: 29.46531  | Val Loss: 0.3774 Accuracy: 0.9042\n",
      "Epoch: 122/150 Train Loss: 0.1104 Accuracy: 0.9653 Time: 29.47666  | Val Loss: 0.3807 Accuracy: 0.9050\n",
      "Early stop at epoch 122!\n",
      "#Parameter: 58164298 Accuracy: 0.9054777070063694\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet150_config\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(64, train_dataset, val_dataset)\n",
    "net, resnet150_stats = train_net(\n",
    "    resnet150_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-4,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f00e0fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = resnet18_stats.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(resnet18_stats[field], label=\"Resnet18\", linestyle=\"--\")\n",
    "    plt.plot(resnet34_stats[field], label=\"Resnet34\")\n",
    "    plt.plot(resnet50_stats[field], label=\"Resnet50\", linestyle=\"--\")\n",
    "    plt.plot(resnet101_stats[field], label=\"Resnet101\")\n",
    "    plt.plot(resnet150_stats[field], label=\"Resnet150\", linestyle=\"--\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71fae624",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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